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Books > Science & Mathematics > Mathematics > Probability & statistics
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Geographic Data Science with R - Visualizing and Analyzing Environmental Change (Hardcover)
Loot Price: R2,578
Discovery Miles 25 780
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Geographic Data Science with R - Visualizing and Analyzing Environmental Change (Hardcover)
Series: Chapman & Hall/CRC Data Science Series
Expected to ship within 12 - 19 working days
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The burgeoning field of data science has provided a wealth of
techniques for analysing large and complex geospatial datasets,
including descriptive, explanatory, and predictive analytics.
However, applying these methods is just one part of the overall
process of geographic data science. Other critical steps include
screening for suspect data values, handling missing data,
harmonizing data from multiple sources, summarizing the data, and
visualizing data and analysis results. Although there are many
books available on statistical and machine learning methods, few
encompass the broader topic of scientific workflows for geospatial
data processing and analysis. The purpose of Geographic Data
Science with R is to fill this gap by providing a series of
tutorials aimed at teaching good practices for using geospatial
data to address problems in environmental geography. It is based on
the R language and environment, which currently provides the best
option for working with diverse spatial and non-spatial data in a
single platform. Fundamental techniques for processing and
visualizing tabular, vector, and raster data are introduced through
a series of practical examples followed by case studies that
combine multiple types of data to address more complex problems.
The book will have a broad audience. Both students and
professionals can use it as a workbook to learn high-level
techniques for geospatial data processing and analysis with R. It
is also suitable as a textbook. Although not intended to provide a
comprehensive introduction to R, it is designed to be accessible to
readers who have at least some knowledge of coding but little to no
experience with R. Key Features: Focus on developing practical
workflows for processing and integrating multiple sources of
geospatial data in R Example-based approach that teaches R
programming and data science concepts through real-world
applications related to climate, land cover and land use, and
natural hazards. Consistent use of tidyverse packages for tabular
data manipulation and visualization. Strong focus on analysing
continuous and categorical raster datasets using the new terra
package Organized so that each chapter builds on the topics and
techniques covered in the preceding chapters Can be used for
self-study or as the textbook for a geospatial science course.
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